Gastos Casa
31 March, 2025
1. Gastos (cálculos antiguos)
Gastos_casa %>%
dplyr::select(-Tiempo,-link) %>%
dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>%
knitr::kable(format = "markdown", size=12)| fecha | gasto | monto | gastador | obs |
|---|---|---|---|---|
| 21/1/2025 | Comida | 21525 | Andrés | piwen |
| 23/1/2025 | VTR | 21990 | Andrés | NA |
| 25/1/2025 | Diosi | 20000 | Andrés | arena diosi |
| 27/1/2025 | Comida | 71516 | Tami | Supermercado |
| 30/1/2025 | Electricidad | 55000 | Andrés | NA |
| 6/2/2025 | Comida | 52730 | Andrés | supermercado (no cobre el otro de 25k pq muchas son cosas mías) |
| 9/2/2025 | Comida | 12500 | Andrés | NA |
| 17/2/2025 | Comida | 7940 | Andrés | NA |
| 18/2/2025 | Electricidad | 64888 | Andrés | la puse por adelantado para que no se me olvide |
| 18/2/2025 | Comida | 17820 | Tami | Supermercado |
| 23/2/2025 | Comida | 86908 | Tami | Supermercado |
| 27/2/2025 | Comida | 10000 | Andrés | NA |
| 26/2/2025 | Comida | 4620 | Andrés | NA |
| 1/3/2025 | Comida | 2300 | Tami | Supermercado |
| 2/3/2025 | Comida | 102058 | Tami | Supermercado |
| 3/3/2025 | Comida | 9370 | Andrés | NA |
| 9/3/2025 | Comida | 61916 | Tami | Supermercado |
| 11/3/2025 | Comida | 27021 | Andrés | NA |
| 11/3/2025 | Enceres | 13190 | Tami | 40 rollos confort |
| 15/3/2025 | Comida | 78061 | Tami | Supermercado |
| 17/3/2025 | Electricidad | 52458 | Andrés | NA |
| 17/3/2025 | VTR | 22000 | Andrés | NA |
| 21/3/2025 | Agua | 19562 | Andrés | NA |
| 22/3/2025 | Comida | 76766 | Tami | Supermercado |
| 21/3/2025 | Diosi | 18500 | Andrés | antiparasitario |
| 27/3/2025 | Gas | 82450 | Andrés | NA |
| 26/3/2025 | Comida | 4000 | Andrés | avena multigrano y chucrut |
| 29/3/2025 | Comida | 70591 | Tami | Supermercado |
| 31/3/2019 | Comida | 9000 | Andrés | NA |
| 8/9/2019 | Comida | 24588 | Andrés | Super Lider |
#para ver las diferencias depués de la diosi
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::group_by(gastador, fecha,.drop = F) %>%
dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>%
dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
#dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv)
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")
par(mfrow=c(1,2))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))
library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
# dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
#dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>%
dplyr::group_by(gastador_nombre, fecha_simp) %>%
dplyr::summarise(monto_total=sum(monto)) %>%
dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
ggplot(aes(hover_css = "fill:none;")) +#, ) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
# guides(color = F)+
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# x <- girafe(ggobj = gg)
# x <- girafe_options(x = x,
# opts_hover(css = "stroke:red;fill:orange") )
# if( interactive() ) print(x)
#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"
#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )
x <- girafe(ggobj = gg)
x <- girafe_options(x,
opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))xplot<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
dplyr::group_by(month)%>%
dplyr::summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = month, y = gasto_total)) +
geom_point()+
geom_line(size=1) +
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Mes") +
scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot) plot2<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = day, y = gasto_total)) +
geom_line(size=1) +
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Día") +
scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot2) tsData <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto))%>%
dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
tsData_gastos = decompose(tsData_gastos)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
# Assuming your time series starts on "2019-03-03"
start_date <- as.Date("2019-03-03")
frequency <- 7 # Weekly data
num_periods <- length(tsData_gastos$x) # Total number of periods in your time series
# Generate sequence of dates
dates <- tsData$day# seq.Date(from = start_date, by = "day", length.out = num_periods)
# Create a data frame from the decomposed time series object
tsData_gastos_df <- data.frame(
day = dates,
Actual = as.numeric(tsData_gastos$x),
Seasonal = as.numeric(tsData_gastos$seasonal),
Trend = as.numeric(tsData_gastos$trend),
Random = as.numeric(tsData_gastos$random)
)
tsData_gastos_long <- tsData_gastos_df %>%
pivot_longer(cols = c("Actual", "Seasonal", "Trend", "Random"),
names_to = "Component", values_to = "Value")
# Plotting with facet_wrap
ggplot(tsData_gastos_long, aes(x = day, y = Value)) +
geom_line() +
theme_bw() +
labs(title = "Descomposición de los Gastos Diarios", x = "Date", y = "Value") +
scale_x_date(date_breaks = "3 months", date_labels = "%m %Y") +
facet_wrap(~ Component, scales = "free_y", ncol=1) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))+
theme(strip.text = element_text(size = 12))
#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
#it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
#ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan.
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
itsa_metro_region_quar2<-
its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
interrupt_var = "covid",
alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 
print(itsa_metro_region_quar2)## [[1]]
## [1] "ITSA Model Fit"
##
## $aov.result
## Anova Table (Type II tests)
##
## Response: depvar
## Sum Sq Df F value Pr(>F)
## interrupt_var 1.0026e+09 2 5.0946 0.0063 **
## lag_depvar 2.6288e+11 1 2671.5603 <2e-16 ***
## Residuals 8.1180e+10 825
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $tukey.result
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
##
## $`x$interrupt_var`
## diff lwr upr p adj
## 1-0 7228.838 -1877.312 16334.99 0.1499295
## 2-0 31359.153 23161.704 39556.60 0.0000000
## 2-1 24130.315 19381.855 28878.77 0.0000000
##
##
## $data
## depvar interrupt_var lag_depvar
## 2 19269.29 0 16010.00
## 3 24139.00 0 19269.29
## 4 23816.14 0 24139.00
## 5 26510.14 0 23816.14
## 6 23456.71 0 26510.14
## 7 24276.71 0 23456.71
## 8 18818.71 0 24276.71
## 9 18517.14 0 18818.71
## 10 15475.29 0 18517.14
## 11 16365.29 0 15475.29
## 12 12621.29 0 16365.29
## 13 12679.86 0 12621.29
## 14 13440.71 0 12679.86
## 15 15382.86 0 13440.71
## 16 13459.71 0 15382.86
## 17 14644.14 0 13459.71
## 18 13927.00 0 14644.14
## 19 22034.57 0 13927.00
## 20 20986.00 0 22034.57
## 21 20390.57 0 20986.00
## 22 22554.14 0 20390.57
## 23 21782.57 0 22554.14
## 24 22529.57 0 21782.57
## 25 24642.71 0 22529.57
## 26 17692.29 0 24642.71
## 27 19668.29 0 17692.29
## 28 28640.00 0 19668.29
## 29 28706.00 0 28640.00
## 30 28331.57 0 28706.00
## 31 25617.86 0 28331.57
## 32 27223.29 0 25617.86
## 33 31622.57 0 27223.29
## 34 32021.43 0 31622.57
## 35 33634.57 0 32021.43
## 36 30784.86 0 33634.57
## 37 34770.57 0 30784.86
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## 686 39678.86 2 39336.29
## 687 41963.14 2 39678.86
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## 690 73116.00 2 63901.86
## 691 60863.86 2 73116.00
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## 693 52725.00 2 56293.86
## 694 58625.00 2 52725.00
## 695 47513.00 2 58625.00
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## 706 99637.71 2 79551.14
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## 708 98395.14 2 95424.29
## 709 115594.71 2 98395.14
## 710 114267.57 2 115594.71
## 711 88353.29 2 114267.57
## 712 88750.86 2 88353.29
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## 727 33690.71 2 32975.00
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## 740 42783.29 2 41063.29
## 741 45952.57 2 42783.29
## 742 44937.43 2 45952.57
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## 746 67134.29 2 43139.14
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## 748 68770.71 2 73224.29
## 749 59539.29 2 68770.71
## 750 82179.86 2 59539.29
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## 752 73015.00 2 74252.14
## 753 56116.43 2 73015.00
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## 756 136678.00 2 131425.14
## 757 115531.29 2 136678.00
## 758 118310.86 2 115531.29
## 759 117449.43 2 118310.86
## 760 115193.57 2 117449.43
## 761 61025.43 2 115193.57
## 762 43913.86 2 61025.43
## 763 46099.29 2 43913.86
## 764 44524.86 2 46099.29
## 765 42208.71 2 44524.86
## 766 166486.57 2 42208.71
## 767 171565.29 2 166486.57
## 768 200415.71 2 171565.29
## 769 204498.14 2 200415.71
## 770 197558.86 2 204498.14
## 771 195266.57 2 197558.86
## 772 203144.29 2 195266.57
## 773 85493.71 2 203144.29
## 774 74721.57 2 85493.71
## 775 36232.14 2 74721.57
## 776 40161.71 2 36232.14
## 777 40629.86 2 40161.71
## 778 45663.71 2 40629.86
## 779 39252.29 2 45663.71
## 780 39618.57 2 39252.29
## 781 39438.43 2 39618.57
## 782 44650.71 2 39438.43
## 783 38626.71 2 44650.71
## 784 38280.43 2 38626.71
## 785 44134.14 2 38280.43
## 786 47596.43 2 44134.14
## 787 45598.43 2 47596.43
## 788 42564.29 2 45598.43
## 789 45699.14 2 42564.29
## 790 49553.86 2 45699.14
## 791 50018.43 2 49553.86
## 792 43772.86 2 50018.43
## 793 39235.43 2 43772.86
## 794 39905.00 2 39235.43
## 795 40374.43 2 39905.00
## 796 34230.57 2 40374.43
## 797 34324.14 2 34230.57
## 798 33491.57 2 34324.14
## 799 33366.43 2 33491.57
## 800 46646.86 2 33366.43
## 801 49770.86 2 46646.86
## 802 57339.86 2 49770.86
## 803 59799.14 2 57339.86
## 804 53577.14 2 59799.14
## 805 61775.29 2 53577.14
## 806 70627.86 2 61775.29
## 807 57888.43 2 70627.86
## 808 49960.71 2 57888.43
## 809 42923.71 2 49960.71
## 810 47284.86 2 42923.71
## 811 52284.86 2 47284.86
## 812 50191.00 2 52284.86
## 813 36465.86 2 50191.00
## 814 34525.14 2 36465.86
## 815 43199.14 2 34525.14
## 816 52757.43 2 43199.14
## 817 43200.86 2 52757.43
## 818 36772.29 2 43200.86
## 819 29568.00 2 36772.29
## 820 42362.00 2 29568.00
## 821 42566.29 2 42362.00
## 822 39596.00 2 42566.29
## 823 32925.00 2 39596.00
## 824 43416.57 2 32925.00
## 825 52624.86 2 43416.57
## 826 57733.71 2 52624.86
## 827 54120.57 2 57733.71
## 828 53353.43 2 54120.57
## 829 56286.86 2 53353.43
## 830 60626.86 2 56286.86
##
## $alpha
## [1] 0.05
##
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
##
## $group.means
## interrupt_var count mean s.d.
## 1 0 37 22066.04 6308.636
## 2 1 120 29463.10 9187.258
## 3 2 673 53593.41 22247.957
##
## $dependent
## [1] 19269.29 24139.00 23816.14 26510.14 23456.71 24276.71 18818.71
## [8] 18517.14 15475.29 16365.29 12621.29 12679.86 13440.71 15382.86
## [15] 13459.71 14644.14 13927.00 22034.57 20986.00 20390.57 22554.14
## [22] 21782.57 22529.57 24642.71 17692.29 19668.29 28640.00 28706.00
## [29] 28331.57 25617.86 27223.29 31622.57 32021.43 33634.57 30784.86
## [36] 34770.57 38443.00 35073.00 31422.29 30103.29 19319.29 27926.29
## [43] 30715.43 31962.29 39790.14 39211.57 44548.57 49398.00 41039.00
## [50] 34821.29 29123.57 21275.71 28476.14 24561.86 20323.57 25370.00
## [57] 26811.86 27151.86 27623.29 22896.57 41889.29 44000.14 38558.00
## [64] 43373.86 49001.00 61213.29 58939.57 42046.86 39191.71 42646.43
## [71] 36121.57 30915.57 20273.43 23938.29 19274.29 21662.29 15819.00
## [78] 18126.14 17240.71 16127.71 13917.14 15379.86 19510.14 24567.29
## [85] 25700.43 25729.00 26435.00 31157.14 29818.43 30962.43 28746.71
## [92] 27830.71 28252.14 28717.57 21365.43 24816.86 16838.57 15529.14
## [99] 13286.29 13629.43 14404.86 19524.86 18475.71 22495.00 22254.57
## [106] 24173.29 27466.43 24602.43 20531.14 20846.43 23875.71 36312.71
## [113] 34244.00 36347.43 39779.71 42018.71 39372.57 33444.00 29255.86
## [120] 31640.14 29671.14 31023.71 39723.43 39314.14 38239.86 34649.43
## [127] 36688.43 42867.57 42226.86 32155.14 33603.00 37254.43 33145.57
## [134] 31299.43 30252.00 26310.71 27929.86 27666.14 25017.57 27335.00
## [141] 25760.71 18436.86 21906.00 19418.14 22826.14 23444.29 25264.86
## [148] 25473.29 27366.86 28855.86 32326.86 27141.43 26297.71 23499.14
## [155] 30246.29 39931.86 38020.43 35004.00 40750.86 42363.29 46273.57
## [162] 41083.29 35711.29 41921.71 60583.29 63115.57 61300.14 57666.43
## [169] 55834.00 58927.71 57810.57 48987.14 52219.29 56503.57 56545.00
## [176] 64705.57 53833.29 50114.00 39592.43 29907.29 33923.29 45489.00
## [183] 44866.29 51680.57 58257.00 70600.57 76648.00 69430.14 69651.57
## [190] 77745.14 72795.86 67670.71 55357.86 48524.00 50154.43 45111.57
## [197] 36147.00 43501.57 41472.43 41058.00 41605.57 49382.86 59558.57
## [204] 59134.57 61109.00 63004.43 67344.29 78180.86 69117.86 55597.57
## [211] 49426.14 39119.43 35636.86 39201.14 27777.00 47207.00 55587.29
## [218] 56619.71 82679.86 91259.57 93552.71 102242.71 91884.00 85013.86
## [225] 84535.29 80700.43 79740.57 85163.14 86724.86 80355.00 74875.14
## [232] 81347.00 66062.43 56946.43 47732.14 38129.71 42928.29 45392.57
## [239] 37895.43 30660.29 42430.86 35845.14 40350.43 31494.71 30013.29
## [246] 34197.57 37430.14 26932.43 33729.86 38081.43 44028.00 47139.71
## [253] 46558.86 58350.57 78380.00 78168.29 70510.86 72207.14 67881.00
## [260] 69536.43 62390.71 50113.14 45565.57 45805.29 41348.57 51426.86
## [267] 47160.57 51907.43 49751.43 54407.43 54746.29 61634.57 58926.43
## [274] 69999.29 63044.86 63285.29 61395.43 67969.43 60792.57 56859.14
## [281] 44899.43 43064.14 62790.29 69120.71 69589.43 66633.29 65588.57
## [288] 70168.57 74644.71 52891.00 41560.57 34704.86 46520.00 50231.00
## [295] 49216.71 76914.86 83720.71 84485.00 89765.00 87702.86 82013.86
## [302] 85982.43 57248.43 52968.43 52601.86 45493.29 42298.86 46423.71
## [309] 37898.00 36435.14 30209.57 34541.86 33604.71 37990.71 35683.43
## [316] 65201.86 62730.57 64589.14 73744.86 76477.71 105647.43 103790.29
## [323] 76122.29 74746.14 72865.71 63652.57 60358.29 25957.14 30178.43
## [330] 30681.57 33337.29 32582.71 39184.43 40415.71 34975.43 34076.14
## [337] 34221.14 28862.57 35729.86 36489.29 36785.14 37787.71 39832.14
## [344] 41917.86 41633.57 33557.00 22759.57 28877.86 27574.00 27104.71
## [351] 24376.14 29732.29 34030.00 39139.71 37066.57 38509.29 40957.29
## [358] 49423.00 50053.29 50284.14 53103.86 50223.00 49587.14 41167.71
## [365] 37958.71 33582.29 31039.43 26526.57 34869.43 37487.43 46514.43
## [372] 39613.43 38980.57 37306.14 36771.29 26317.00 31580.71 23626.57
## [379] 33035.71 44864.57 48946.14 46969.57 49249.57 56370.14 67228.71
## [386] 59457.29 53124.71 52814.14 61262.00 61861.14 71784.71 59313.29
## [393] 61107.00 60603.43 60012.57 58280.43 56862.71 41704.43 51533.00
## [400] 50388.71 49205.29 56533.29 47996.14 47207.57 45292.00 40343.43
## [407] 39004.86 36788.43 30027.57 39040.14 42390.14 36291.14 30668.29
## [414] 47693.00 52094.43 56592.57 47971.43 43762.43 42246.71 46352.43
## [421] 33094.86 32784.86 26212.43 32611.57 42144.86 50034.86 46332.00
## [428] 42976.29 39456.29 39328.29 35296.14 30875.43 27709.00 29513.29
## [435] 31630.43 29346.14 34916.86 42020.86 38303.00 37966.43 41408.14
## [442] 38988.14 43555.29 38114.00 27847.86 26517.00 39518.29 39153.71
## [449] 45623.14 40627.43 41027.71 42882.86 47139.43 35547.57 41099.00
## [456] 35859.57 44524.57 48554.29 51554.29 47810.29 50490.00 50720.71
## [463] 52720.71 52145.57 55515.57 52457.00 58239.57 50523.57 47788.57
## [470] 46170.00 42305.57 46605.57 55149.57 48769.57 50719.43 44753.71
## [477] 42898.00 46141.14 34022.57 26651.86 28791.86 31879.00 33584.71
## [484] 34690.43 27410.43 41755.00 49379.57 57198.86 51144.57 56677.43
## [491] 65416.43 69779.71 54046.00 43259.57 40998.57 41368.57 42274.29
## [498] 35962.71 38709.00 44778.14 51282.43 52094.86 52221.43 45011.43
## [505] 46545.43 42263.00 45417.43 45034.71 37840.57 39135.43 38191.14
## [512] 39456.86 42479.14 34282.57 28878.43 56227.14 65569.43 69751.29
## [519] 62171.71 63705.14 79257.86 87244.71 58568.00 52695.29 48911.00
## [526] 53924.00 53358.86 42121.14 47835.71 62329.29 56056.86 59946.43
## [533] 64511.57 61137.43 55448.71 47964.43 46425.71 55512.00 55226.29
## [540] 46709.14 49254.71 49056.29 49850.57 39145.71 29799.43 34769.86
## [547] 44061.57 43829.14 45782.00 38924.57 49242.43 50565.00 38864.43
## [554] 49786.71 58787.86 58060.86 62179.43 57333.86 70797.00 89901.71
## [561] 78558.14 65466.00 70525.00 68377.86 69736.29 60085.86 41757.00
## [568] 49780.29 56540.29 57894.29 60270.29 61011.00 57721.43 71741.00
## [575] 59576.00 52390.29 61092.29 62814.00 54908.29 62082.00 57017.71
## [582] 53634.43 69169.00 52488.14 60895.57 59856.57 52670.00 51874.57
## [589] 52190.57 41562.43 44764.14 38612.71 43473.14 53505.00 45870.86
## [596] 52578.00 55300.00 61789.71 57391.71 62902.29 53250.43 55402.57
## [603] 56291.29 58933.57 59590.71 59065.00 52399.57 60483.43 58262.71
## [610] 54939.71 51169.00 43113.29 56289.71 60739.86 50363.14 62270.86
## [617] 67061.57 59609.00 85054.00 68023.29 59242.29 61535.14 56215.86
## [624] 45152.29 57409.57 35151.43 34991.43 45944.71 57944.71 55706.29
## [631] 88593.71 77359.43 79878.71 81753.00 75716.00 67381.43 63528.57
## [638] 49682.86 47815.00 46546.14 44808.71 42959.57 46023.86 51309.57
## [645] 68447.29 84959.29 81666.29 82700.86 89422.14 104812.71 98812.71
## [652] 64779.86 61862.86 58376.43 59503.57 55429.43 44454.57 47184.00
## [659] 52126.71 51202.00 64437.14 64297.14 64628.57 51413.14 52969.43
## [666] 54135.29 48799.43 41907.86 45382.00 42633.29 46624.71 44051.86
## [673] 35852.86 29737.71 29734.86 32881.71 38298.57 40886.14 38601.86
## [680] 38628.86 39142.57 32666.14 39911.57 39336.29 39678.86 41963.14
## [687] 54220.57 63901.86 73116.00 60863.86 56293.86 52725.00 58625.00
## [694] 47513.00 40300.14 33312.43 29556.71 27816.71 34120.29 32132.57
## [701] 32902.57 39694.14 72501.29 79551.14 99637.71 95424.29 98395.14
## [708] 115594.71 114267.57 88353.29 88750.86 78835.71 75519.14 73202.86
## [715] 53433.29 48165.71 52163.14 49306.86 36846.86 43220.57 38952.29
## [722] 41522.29 39090.00 28452.57 32975.00 33690.71 26405.29 47087.43
## [729] 49660.29 47409.71 53881.71 45189.57 45503.86 54640.14 39131.29
## [736] 35024.14 44755.43 41063.29 42783.29 45952.57 44937.43 40838.43
## [743] 48838.43 43139.14 67134.29 73224.29 68770.71 59539.29 82179.86
## [750] 74252.14 73015.00 56116.43 111885.00 131425.14 136678.00 115531.29
## [757] 118310.86 117449.43 115193.57 61025.43 43913.86 46099.29 44524.86
## [764] 42208.71 166486.57 171565.29 200415.71 204498.14 197558.86 195266.57
## [771] 203144.29 85493.71 74721.57 36232.14 40161.71 40629.86 45663.71
## [778] 39252.29 39618.57 39438.43 44650.71 38626.71 38280.43 44134.14
## [785] 47596.43 45598.43 42564.29 45699.14 49553.86 50018.43 43772.86
## [792] 39235.43 39905.00 40374.43 34230.57 34324.14 33491.57 33366.43
## [799] 46646.86 49770.86 57339.86 59799.14 53577.14 61775.29 70627.86
## [806] 57888.43 49960.71 42923.71 47284.86 52284.86 50191.00 36465.86
## [813] 34525.14 43199.14 52757.43 43200.86 36772.29 29568.00 42362.00
## [820] 42566.29 39596.00 32925.00 43416.57 52624.86 57733.71 54120.57
## [827] 53353.43 56286.86 60626.86
##
## $interrupt_var
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [593] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [630] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [667] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [704] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [741] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [778] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [815] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
##
## $residuals
## 2 3 4 5 6 7
## 2020.67508 4041.13853 -538.79785 2437.44221 -2971.06734 518.22425
## 8 9 10 11 12 13
## -5656.61554 -1186.83138 -3965.05633 -415.88043 -4937.91386 -1606.35685
## 14 15 16 17 18 19
## -896.70255 380.30290 -3240.65128 -375.02109 -2127.58533 6606.90871
## 20 21 22 23 24 25
## -1529.26005 -1208.03286 1476.05917 -1186.89530 234.60848 1694.72778
## 26 27 28 29 30 31
## -7102.99943 949.02955 8193.33478 416.30820 -15.81724 -2402.20799
## 32 33 34 35 36 37
## 1575.53568 4571.36398 1124.38813 2388.85212 -1871.06337 4605.85652
## 38 39 40 41 42 43
## 4302.53597 -2277.88223 -2982.55982 -1110.12413 -10741.06110 7293.25635
## 44 45 46 47 48 49
## 2558.20399 1366.80676 8104.66779 683.02382 6525.80800 6709.65856
## 50 51 52 53 54 55
## -5888.68701 -4799.00640 -5061.22677 -7928.17133 6132.81368 -4076.04973
## 56 57 58 59 60 61
## -4892.48697 3859.02922 889.32441 -31.13971 143.06261 -4995.77211
## 62 63 64 65 66 67
## 18129.01154 3636.53534 -3650.90800 5922.44250 7339.58776 14632.65407
## 68 69 70 71 72 73
## 1683.02229 -13222.02263 -1309.64137 4641.02415 -4903.92649 -4405.92986
## 74 75 76 77 78 79
## -10497.01424 2471.14956 -5396.64974 1068.59512 -6862.26794 553.04530
## 80 81 82 83 84 85
## -2349.27571 -2688.23860 -3925.83114 -530.64666 2320.94173 3767.41002
## 86 87 88 89 90 91
## 479.62455 -482.39170 198.63131 4303.59259 -1163.19474 1151.10241
## 92 93 94 95 96 97
## -2064.69080 -1043.72475 178.46632 275.48420 -7483.53392 2395.10020
## 98 99 100 101 102 103
## -8600.40666 -2935.25881 -4033.42022 -1729.58316 -1254.12831 3187.99601
## 104 105 106 107 108 109
## -2337.02456 2599.41645 -1154.65106 974.24465 2590.05732 -3152.79126
## 110 111 112 113 114 115
## -4720.38287 -846.00008 1907.66448 11696.47845 -1244.59740 2667.29058
## 116 117 118 119 120 121
## 4260.76984 3499.28328 -1104.18188 -4719.50881 -3724.92495 2320.61375
## 122 123 124 125 126 127
## -1732.71658 1341.14454 8858.44783 843.91654 127.42629 -2523.86722
## 128 129 130 131 132 133
## 2653.86701 7050.52653 1008.03728 -8503.56787 1748.93021 4134.64949
## 134 135 136 137 138 139
## -3166.26776 -1420.46880 -854.00876 -3879.63779 1184.95681 -494.20385
## 140 141 142 143 144 145
## -2912.23761 1720.55851 -1879.61136 -7827.23601 2044.38517 -3476.17886
## 146 147 148 149 150 151
## 2106.69309 -254.42016 1025.77395 -357.33167 1354.03257 1187.68214
## 152 153 154 155 156 157
## 3357.00599 -4862.75297 -1173.39221 -3234.39294 5959.24669 9746.50047
## 158 159 160 161 162 163
## -3657.00446 -5002.47198 3381.33153 -30.11291 2470.59602 -6138.04139
## 164 165 166 167 168 169
## -6972.72026 3933.88314 17166.32978 3384.76554 -644.37421 -2691.05015
## 170 171 172 173 174 175
## -1346.90435 3348.70959 -472.94242 -8319.77044 2625.76839 4084.53141
## 176 177 178 179 180 181
## 380.65940 8505.01418 -9501.20119 -3715.99054 -10986.18148 -11473.42060
## 182 183 184 185 186 187
## 1009.28157 9064.22927 -1669.17308 5689.48627 6308.90124 12903.39257
## 188 189 190 191 192 193
## 8160.13435 -4344.35381 2186.88843 10086.88814 -1937.75618 -2736.25886
## 194 195 196 197 198 199
## -10568.74243 -6638.76305 965.78836 -5502.38108 -10058.51267 5132.84109
## 200 201 202 203 204 205
## -3325.63037 -1966.19267 -1056.32991 6242.27167 9619.12271 299.56551
## 206 207 208 209 210 211
## 2644.65270 2814.04595 5496.92917 12539.61975 -5996.65537 -11594.11300
## 212 213 214 215 216 217
## -5946.17719 -10857.86030 -5330.35500 1278.37678 -13261.64635 16155.28070
## 218 219 220 221 222 223
## 7549.96014 1256.38595 26413.98505 12212.05621 7004.85688 13690.20316
## 224 225 226 227 228 229
## -4265.26452 -2079.87252 3447.39968 30.90722 2423.46251 8685.13612
## 230 231 232 233 234 235
## 5506.46630 -2228.63346 -2139.99418 9122.32595 -11819.90977 -7574.21588
## 236 237 238 239 240 241
## -8819.34119 -10366.68848 2826.27672 1095.67592 -8555.73286 -9236.91191
## 242 243 244 245 246 247
## 8858.58427 -8016.90315 2245.58019 -10548.63174 -4288.44034 1190.90263
## 248 249 250 251 252 253
## 765.59297 -12558.01867 3416.45785 1825.75211 3968.20213 1881.45419
## 254 255 256 257 258 259
## -1419.64761 10879.84901 20601.02148 2879.68354 -4592.66549 3797.70498
## 260 261 262 263 264 265
## -2011.32214 3425.99831 -5166.88314 -11197.70781 -5012.28932 -797.11143
## 266 267 268 269 270 271
## -5463.38271 8510.93976 -4565.73160 3910.69062 -2394.98753 4145.77660
## 272 273 274 275 276 277
## 414.38244 7006.44098 -1723.40589 11716.89583 -4917.36762 1402.58672
## 278 279 280 281 282 283
## -697.45185 7528.65156 -5395.16264 -3054.61953 -11575.75075 -2955.91562
## 284 285 286 287 288 289
## 18374.62459 7460.56032 2395.24644 -970.64404 568.88659 6062.17046
## 290 291 292 293 294 295
## 6534.50082 -19132.23457 -11445.67777 -8396.38956 9411.98354 2794.24630
## 296 297 298 299 300 301
## -1464.17657 27120.64965 9712.93248 4527.57281 9139.43816 2461.54640
## 302 303 304 305 306 307
## -1424.73889 7517.12713 -24686.17764 -3847.06232 -472.07972 -7260.19628
## 308 309 310 311 312 313
## -4240.34820 2677.06200 -9454.58122 -3464.30270 -8411.05192 1363.59638
## 314 315 316 317 318 319
## -3360.80841 1844.43707 -4297.06734 27238.37856 -1037.76590 2981.19082
## 320 321 322 323 324 325
## 10512.15150 5241.13021 32021.79481 4664.63744 -21379.85781 1431.22292
## 326 327 328 329 330 331
## 753.81137 -6815.47048 -2055.67399 -33576.96919 717.61904 -2469.46441
## 332 333 334 335 336 337
## -253.59502 -3329.77825 3931.57851 -608.32052 -7124.98989 -3268.40576
## 338 339 340 341 342 343
## -2337.25480 -7822.58448 3729.13687 -1514.78048 -1882.81190 -1138.87727
## 344 345 346 347 348 349
## 29.10851 327.59383 -1780.01261 -9608.06292 -13344.99421 2212.34814
## 350 351 352 353 354 355
## -4440.08297 -3769.54346 -6087.86772 1653.57827 1268.97996 2621.65448
## 356 357 358 359 360 361
## -3918.37438 -663.32925 523.45733 6849.14258 78.74431 -241.39108
## 362 363 364 365 366 367
## 2376.50908 -2969.32782 -1086.75444 -8950.31996 -4799.09872 -6370.23596
## 368 369 370 371 372 373
## -5087.24171 -7377.14619 4910.82751 235.54461 6973.90245 -7818.45462
## 374 375 376 377 378 379
## -2418.49298 -3539.68108 -2610.76136 -12597.47771 1805.31949 -10750.33538
## 380 381 382 383 384 385
## 5612.27834 9215.71109 2956.55632 -2588.10391 1419.80469 6547.21182
## 386 387 388 389 390 391
## 11181.01626 -6082.91972 -5621.74818 -396.41817 8322.93892 1537.00842
## 392 393 394 395 396 397
## 10936.81224 -10209.75154 2486.42159 414.79434 264.15676 -951.46182
## 398 399 400 401 402 403
## -854.94571 -14773.87287 8305.99413 -1430.37829 -1613.47816 6749.06902
## 404 405 406 407 408 409
## -8194.17382 -1519.61878 -2745.82511 -6019.81383 -3032.36952 -4078.62582
## 410 411 412 413 414 415
## -8901.89248 6020.98556 1492.24188 -7535.31093 -7826.45357 14113.73357
## 416 417 418 419 420 421
## 3632.24429 4282.68088 -8270.71537 -4943.15654 -2779.38463 2651.35931
## 422 423 424 425 426 427
## -14195.40648 -2915.70558 -9217.13374 2927.59244 6866.78031 6422.83087
## 428 429 430 431 432 433
## -4177.42377 -4296.11935 -4882.57115 -1933.40521 -5853.65113 -6749.48682
## 434 435 436 437 438 439
## -6051.34966 -1478.98834 -939.14279 -5074.22392 2493.40121 4727.51137
## 440 441 442 443 444 445
## -5200.62611 -2287.06590 1448.87740 -3979.85157 2702.84287 -6731.01569
## 446 447 448 449 450 451
## -12240.41450 -4596.66225 9568.05194 -2162.17672 4625.95832 -6025.29722
## 452 453 454 455 456 457
## -1257.78372 247.43142 2882.24649 -12430.68355 3254.28704 -6838.17190
## 458 459 460 461 462 463
## 6407.10967 2861.92543 2339.16988 -4027.41472 1925.28515 -186.59304
## 464 465 466 467 468 469
## 1611.71772 -711.81487 3160.97206 -2843.63607 5612.72280 -7158.37146
## 470 471 472 473 474 475
## -3148.08387 -2375.73233 -4825.21407 2853.04955 7638.01162 -6211.10932
## 476 477 478 479 480 481
## 1316.11107 -6354.15832 -2994.67580 1870.72297 -13082.98730 -9859.70865
## 482 483 484 485 486 487
## -1276.26805 -59.90221 -1052.95236 -1438.36475 -9684.97450 11023.73556
## 488 489 490 491 492 493
## 6108.35627 7262.28078 -5627.58438 5197.89826 9100.10295 5823.79972
## 494 495 496 497 498 499
## -13724.27654 -10756.37284 -3587.93235 -1241.37776 -659.11557 -7762.45778
## 500 501 502 503 504 505
## 501.37128 4169.72526 5368.39744 494.81283 -88.83663 -7409.48472
## 506 507 508 509 510 511
## 427.46027 -5195.98323 1702.12242 -1438.17713 -8297.75312 -713.81322
## 512 513 514 515 516 517
## -2790.05640 -698.85239 1216.95239 -9621.68570 -7860.42788 24212.56035
## 518 519 520 521 522 523
## 9646.74041 5661.61933 -5573.71015 2585.74086 16797.93976 11188.69390
## 524 525 526 527 528 529
## -24470.08990 -5273.76775 -3924.15678 4397.04637 -550.43536 -11294.10466
## 530 531 532 533 534 535
## 4244.41892 13742.34134 -5200.29299 4172.60331 5337.50278 -2027.46453
## 536 537 538 539 540 541
## -4766.52045 -7277.76133 -2273.75146 8157.67039 -71.22823 -8338.60113
## 542 543 544 545 546 547
## 1652.61287 -771.14118 196.60977 -11202.60786 -11190.76241 1950.14116
## 548 549 550 551 552 553
## 6896.73230 -1458.46521 697.57980 -7867.02646 8445.55954 748.31322
## 554 555 556 557 558 559
## -12108.44337 9042.42183 8495.35858 -100.39431 4653.71679 -3792.28864
## 560 561 562 563 564 565
## 13906.82789 21242.13179 -6802.68281 -9978.33375 6525.75034 -43.94435
## 566 567 568 569 570 571
## 3191.50549 -7646.45437 -17538.95639 6507.32217 6253.40698 1697.84967
## 572 573 574 575 576 577
## 2890.18982 1553.81710 -2383.28295 14512.01494 -9908.82245 -6459.95617
## 578 579 580 581 582 583
## 8523.75837 2638.22892 -6772.59915 7312.24998 -4023.25994 -2979.37308
## 584 585 586 587 588 589
## 15512.84936 -14748.25039 8241.49787 -147.23303 -6425.51599 -938.48071
## 590 591 592 593 594 595
## 72.87887 -10831.50957 1661.27265 -7289.07811 2948.89774 8731.79317
## 596 597 598 599 600 601
## -7672.14771 5708.72365 2567.37379 6677.52965 -3393.74527 5961.53519
## 602 603 604 605 606 607
## -8507.63522 2082.11162 1089.43366 2954.80991 1302.08016 201.89496
## 608 609 610 611 612 613
## -6003.95688 7906.78371 -1380.79700 -2762.45997 -3628.22471 -8387.59992
## 614 615 616 617 618 619
## 11831.09273 4762.46936 -9504.53697 11474.44768 5855.49926 -5785.09001
## 620 621 622 623 624 625
## 26174.90968 -13099.69300 -6992.52999 2976.63228 -4347.05738 -10760.53654
## 626 627 628 629 630 631
## 11168.46654 -21804.93258 -2506.97834 8586.17856 11010.87240 -1717.89458
## 632 633 634 635 636 637
## 33126.35676 -6857.95021 5482.29042 5154.22949 -2521.26146 -5578.31848
## 638 639 640 641 642 643
## -2145.13605 -12622.70240 -2386.70718 -2022.69320 -2650.89337 -2981.18508
## 644 645 646 647 648 649
## 1699.61182 4306.54327 16823.51325 18353.81139 626.10574 4539.40086
## 650 651 652 653 654 655
## 10356.26955 19871.12750 416.76895 -28370.91899 -1536.56995 -2472.97209
## 656 657 658 659 660 661
## 1701.98873 -3357.49663 -10770.75900 1552.83335 4109.49519 -1136.11455
## 662 663 664 665 666 667
## 12907.40879 1197.31483 1651.13069 -11854.03104 1255.11450 1060.47466
## 668 669 670 671 672 673
## -5294.56881 -7521.56132 1977.15791 -3808.63423 2585.70626 -3476.43725
## 674 675 676 677 678 679
## -9426.25874 -8373.87788 -3030.90852 118.44632 2784.33710 636.51982
## 680 681 682 683 684 685
## -3909.80756 -1885.89671 -1395.78569 -8321.30065 4585.78853 -2323.41365
## 686 687 688 689 690 691
## -1477.93040 506.88113 10767.39885 9733.30342 10484.11600 -9822.98325
## 692 693 694 695 696 697
## -3682.22285 -3256.00945 5763.86715 -10505.88257 -8004.68635 -8686.95794
## 698 699 700 701 702 703
## -6334.04826 -4790.82211 3033.84839 -4464.41569 -1956.76606 4161.67532
## 704 705 706 707 708 709
## 31031.66129 9401.68254 23325.30504 1552.29882 8206.51359 22808.97696
## 710 711 712 713 714 715
## 6446.05704 -18308.04720 4743.65979 -5519.03797 -167.84241 415.20160
## 716 717 718 719 720 721
## -17329.48475 -5314.59830 3287.71417 -3063.10308 -13026.15277 4240.02956
## 722 723 724 725 726 727
## -5600.12446 701.18900 -3977.77752 -12488.91441 1332.69962 -1905.06994
## 728 729 730 731 732 733
## -9816.17226 17234.85486 1727.48887 -2772.26107 5667.17692 -8682.75512
## 734 735 736 737 738 739
## -769.84273 8091.69603 -15404.05519 -5953.43474 7368.29418 -4830.88870
## 740 741 742 743 744 745
## 116.76363 1782.43418 -2003.28198 -5214.84931 6368.47545 -6324.36920
## 746 747 748 749 750 751
## 22653.05997 7766.62925 -2010.78892 -7348.92821 23361.71069 -4358.27493
## 752 753 754 755 756 757
## 1334.94935 -14482.11814 56059.09770 26846.64165 15017.60620 -10721.12883
## 758 759 760 761 762 763
## 10544.79168 7253.47604 5750.67533 -46445.40879 -16203.46776 940.80872
## 764 765 766 767 768 769
## -2544.11029 -3483.89577 122818.72157 19254.37102 43665.01363 22526.54562
## 770 771 772 773 774 775
## 12018.42181 15792.42405 25674.04274 -98863.18608 -6785.80326 -35858.27984
## 776 777 778 779 780 781
## 1718.55249 -1248.51582 3376.09324 -7435.90741 -1464.78375 -1965.13170
## 782 783 784 785 786 787
## 3404.63398 -7175.91944 -2256.05527 3900.38021 2245.37894 -2779.33346
## 788 789 790 791 792 793
## -4066.83497 1720.45428 2834.69254 -70.50747 -6722.20486 -5799.78694
## 794 795 796 797 798 799
## -1163.61875 -1279.52608 -7833.75527 -2369.25546 -3283.62656 -2680.93974
## 800 801 802 803 804 805
## 10708.88807 2223.20558 7061.22081 2903.72558 -5468.16937 8169.21395
## 806 807 808 809 810 811
## 9855.01097 -10623.29011 -7414.26132 -7520.89418 2991.95795 4179.46925
## 812 813 814 815 816 817
## -2285.36223 -14180.06592 -4122.33076 6248.23170 8223.75114 -9688.62460
## 818 819 820 821 822 823
## -7762.89034 -9347.35190 9744.59769 -1235.56573 -4384.43697 -8458.82844
## 824 825 826 827 828 829
## 7864.49695 7901.10477 4960.12580 -3119.15374 -727.70566 2876.35526
## 830
## 4651.96706
##
## $fitted.values
## 2 3 4 5 6 7 8 9
## 17248.61 20097.86 24354.94 24072.70 26427.78 23758.49 24475.33 19703.97
## 10 11 12 13 14 15 16 17
## 19440.34 16781.17 17559.20 14286.21 14337.42 15002.55 16700.37 15019.16
## 18 19 20 21 22 23 24 25
## 16054.59 15427.66 22515.26 21598.60 21078.08 22969.47 22294.96 22947.99
## 26 27 28 29 30 31 32 33
## 24795.29 18719.26 20446.67 28289.69 28347.39 28020.07 25647.75 27051.21
## 34 35 36 37 38 39 40 41
## 30897.04 31245.72 32655.92 30164.71 34140.46 37350.88 34404.85 31213.41
## 42 43 44 45 46 47 48 49
## 30060.35 20633.03 28157.22 30595.48 31685.48 38528.55 38022.76 42688.34
## 50 51 52 53 54 55 56 57
## 46927.69 39620.29 34184.80 29203.89 22343.33 28637.91 25216.06 21510.97
## 58 59 60 61 62 63 64 65
## 25922.53 27183.00 27480.22 27892.34 23760.27 40363.61 42208.91 37451.41
## 66 67 68 69 70 71 72 73
## 41661.41 46580.63 57256.55 55268.88 40501.36 38005.40 41025.50 35321.50
## 74 75 76 77 78 79 80 81
## 30770.44 21467.14 24670.94 20593.69 22681.27 17573.10 19589.99 18815.95
## 82 83 84 85 86 87 88 89
## 17842.97 15910.50 17189.20 20799.88 25220.80 26211.39 26236.37 26853.55
## 90 91 92 93 94 95 96 97
## 30981.62 29811.33 30811.41 28874.44 28073.68 28442.09 28848.96 22421.76
## 98 99 100 101 102 103 104 105
## 25438.98 18464.40 17319.71 15359.01 15658.99 16336.86 20812.74 19895.58
## 106 107 108 109 110 111 112 113
## 23409.22 23199.04 24876.37 27755.22 25251.53 21692.43 21968.05 24616.24
## 114 115 116 117 118 119 120 121
## 35488.60 33680.14 35518.94 38519.43 40476.75 38163.51 32980.78 29319.53
## 122 123 124 125 126 127 128 129
## 31403.86 29682.57 30864.98 38470.23 38112.43 37173.30 34034.56 35817.04
## 130 131 132 133 134 135 136 137
## 41218.82 40658.71 31854.07 33119.78 36311.84 32719.90 31106.01 30190.35
## 138 139 140 141 142 143 144 145
## 26744.90 28160.35 27929.81 25614.44 27640.33 26264.09 19861.61 22894.32
## 146 147 148 149 150 151 152 153
## 20719.45 23698.71 24239.08 25830.62 26012.82 27668.17 28969.85 32004.18
## 154 155 156 157 158 159 160 161
## 27471.11 26733.54 24287.04 30185.36 41677.43 40006.47 37369.53 42393.40
## 162 163 164 165 166 167 168 169
## 43802.98 47221.33 42684.01 37987.83 43416.96 59730.81 61944.52 60357.48
## 170 171 172 173 174 175 176 177
## 57180.90 55579.00 58283.51 57306.91 49593.52 52419.04 56164.34 56200.56
## 178 179 180 181 182 183 184 185
## 63334.49 53829.99 50578.61 41380.71 32914.00 36424.77 46535.46 45991.09
## 186 187 188 189 190 191 192 193
## 51948.10 57697.18 68487.87 73774.50 67464.68 67658.25 74733.61 70406.97
## 194 195 196 197 198 199 200 201
## 65926.60 55162.76 49188.64 50613.95 46205.51 38368.73 44798.06 43024.19
## 202 203 204 205 206 207 208 209
## 42661.90 43140.59 49939.45 58835.01 58464.35 60190.38 61847.36 65641.24
## 210 211 212 213 214 215 216 217
## 75114.51 67191.68 55372.32 49977.29 40967.21 37922.77 41038.65 31051.72
## 218 219 220 221 222 223 224 225
## 48037.33 55363.33 56265.87 79047.52 86547.86 88552.51 96149.26 87093.73
## 226 227 228 229 230 231 232 233
## 81087.89 80669.52 77317.11 76478.01 81218.39 82583.63 77015.14 72224.67
## 234 235 236 237 238 239 240 241
## 77882.34 64520.64 56551.48 48496.40 40102.01 44296.90 46451.16 39897.20
## 242 243 244 245 246 247 248 249
## 33572.27 43862.05 38104.85 42043.35 34301.73 33006.67 36664.55 39490.45
## 250 251 252 253 254 255 256 257
## 30313.40 36255.68 40059.80 45258.26 47978.50 47470.72 57778.98 75288.60
## 258 259 260 261 262 263 264 265
## 75103.52 68409.44 69892.32 66110.43 67557.60 61310.85 50577.86 46602.40
## 266 267 268 269 270 271 272 273
## 46811.95 42915.92 51726.30 47996.74 52146.42 50261.65 54331.90 54628.13
## 274 275 276 277 278 279 280 281
## 60649.83 58282.39 67962.22 61882.70 62092.88 60440.78 66187.73 59913.76
## 282 283 284 285 286 287 288 289
## 56475.18 46020.06 44415.66 61660.15 67194.18 67603.93 65019.68 64106.40
## 290 291 292 293 294 295 296 297
## 68110.21 72023.23 53006.25 43101.25 37108.02 47436.75 50680.89 49794.21
## 298 299 300 301 302 303 304 305
## 74007.78 79957.43 80625.56 85241.31 83438.60 78465.30 81934.61 56815.49
## 306 307 308 309 310 311 312 313
## 53073.94 52753.48 46539.21 43746.65 47352.58 39899.45 38620.62 33178.26
## 314 315 316 317 318 319 320 321
## 36965.52 36146.28 39980.50 37963.48 63768.34 61607.95 63232.71 71236.58
## 322 323 324 325 326 327 328 329
## 73625.63 99125.65 97502.14 73314.92 72111.90 70468.04 62413.96 59534.11
## 330 331 332 333 334 335 336 337
## 29460.81 33151.04 33590.88 35912.49 35252.85 41024.03 42100.42 37344.55
## 338 339 340 341 342 343 344 345
## 36558.40 36685.16 32000.72 38004.07 38667.95 38926.59 39803.03 41590.26
## 346 347 348 349 350 351 352 353
## 43413.58 43165.06 36104.57 26665.51 32014.08 30874.26 30464.01 28078.71
## 354 355 356 357 358 359 360 361
## 32761.02 36518.06 40984.95 39172.61 40433.83 42573.86 49974.54 50525.53
## 362 363 364 365 366 367 368 369
## 50727.35 53192.33 50673.90 50118.03 42757.81 39952.52 36126.67 33903.72
## 370 371 372 373 374 375 376 377
## 29958.60 37251.88 39540.53 47431.88 41399.06 40845.82 39382.05 38914.48
## 378 379 380 381 382 383 384 385
## 29775.39 34376.91 27423.44 35648.86 45989.59 49557.68 47829.77 49822.93
## 386 387 388 389 390 391 392 393
## 56047.70 65540.21 58746.46 53210.56 52939.06 60324.13 60847.90 69523.04
## 394 395 396 397 398 399 400 401
## 58620.58 60188.63 59748.41 59231.89 57717.66 56478.30 43227.01 51819.09
## 402 403 404 405 406 407 408 409
## 50818.76 49784.22 56190.32 48727.19 48037.83 46363.24 42037.23 40867.05
## 410 411 412 413 414 415 416 417
## 38929.46 33019.16 40897.90 43826.45 38494.74 33579.27 48462.18 52309.89
## 418 419 420 421 422 423 424 425
## 56242.14 48705.59 45026.10 43701.07 47290.26 35700.56 35429.56 29683.98
## 426 427 428 429 430 431 432 433
## 35278.08 43612.03 50509.42 47272.41 44338.86 41261.69 41149.79 37624.92
## 434 435 436 437 438 439 440 441
## 33760.35 30992.27 32569.57 34420.37 32423.46 37293.35 43503.63 40253.49
## 442 443 444 445 446 447 448 449
## 39959.27 42967.99 40852.44 44845.02 40088.27 31113.66 29950.23 41315.89
## 450 451 452 453 454 455 456 457
## 40997.18 46652.73 42285.50 42635.43 44257.18 47978.25 37844.71 42697.74
## 458 459 460 461 462 463 464 465
## 38117.46 45692.36 49215.12 51837.70 48564.71 50907.31 51109.00 52857.39
## 466 467 468 469 470 471 472 473
## 52354.60 55300.64 52626.85 57681.94 50936.66 48545.73 47130.79 43752.52
## 474 475 476 477 478 479 480 481
## 47511.56 54980.68 49403.32 51107.87 45892.68 44270.42 47105.56 36511.57
## 482 483 484 485 486 487 488 489
## 30068.13 31938.90 34637.67 36128.79 37095.40 30731.26 43271.22 49936.58
## 490 491 492 493 494 495 496 497
## 56772.16 51479.53 56316.33 63955.91 67770.28 54015.94 44586.50 42609.95
## 498 499 500 501 502 503 504 505
## 42933.40 43725.17 38207.63 40608.42 45914.03 51600.04 52310.27 52420.91
## 506 507 508 509 510 511 512 513
## 46117.97 47458.98 43715.31 46472.89 46138.32 39849.24 40981.20 40155.71
## 514 515 516 517 518 519 520 521
## 41262.19 43904.26 36738.86 32014.58 55922.69 64089.67 67745.42 61119.40
## 522 523 524 525 526 527 528 529
## 62459.92 76056.02 83038.09 57969.05 52835.16 49526.95 53909.29 53415.25
## 530 531 532 533 534 535 536 537
## 43591.30 48586.94 61257.15 55773.83 59174.07 63164.89 60215.23 55242.19
## 538 539 540 541 542 543 544 545
## 48699.47 47354.33 55297.51 55047.74 47602.10 49827.43 49653.96 50348.32
## 546 547 548 549 550 551 552 553
## 40990.19 32819.72 37164.84 45287.61 45084.42 46791.60 40796.87 49816.69
## 554 555 556 557 558 559 560 561
## 50972.87 40744.29 50292.50 58161.25 57525.71 61126.15 56890.17 68659.58
## 562 563 564 565 566 567 568 569
## 85360.83 75444.33 63999.25 68421.80 66544.78 67732.31 59295.96 43272.96
## 570 571 572 573 574 575 576 577
## 50286.88 56196.44 57380.10 59457.18 60104.71 57228.99 69484.82 58850.24
## 578 579 580 581 582 583 584 585
## 52568.53 60175.77 61680.88 54769.75 61040.97 56613.80 53656.15 67236.39
## 586 587 588 589 590 591 592 593
## 52654.07 60003.80 59095.52 52813.05 52117.69 52393.94 43102.87 45901.79
## 594 595 596 597 598 599 600 601
## 40524.25 44773.21 53543.00 46869.28 52732.63 55112.18 60785.46 56940.75
## 602 603 604 605 606 607 608 609
## 61758.06 53320.46 55201.85 55978.76 58288.63 58863.11 58403.53 52576.64
## 610 611 612 613 614 615 616 617
## 59643.51 57702.17 54797.22 51500.89 44458.62 55977.39 59867.68 50796.41
## 618 619 620 621 622 623 624 625
## 61206.07 65394.09 58879.09 81122.98 66234.82 58558.51 60562.91 55912.82
## 626 627 628 629 630 631 632 633
## 46241.10 56956.36 37498.41 37358.54 46933.84 57424.18 55467.36 84217.38
## 634 635 636 637 638 639 640 641
## 74396.42 76598.77 78237.26 72959.75 65673.71 62305.56 50201.71 48568.84
## 642 643 644 645 646 647 648 649
## 47459.61 45940.76 44324.25 47003.03 51623.77 66605.47 81040.18 78161.46
## 650 651 652 653 654 655 656 657
## 79065.87 84941.59 98395.95 93150.78 63399.43 60849.40 57801.58 58786.93
## 658 659 660 661 662 663 664 665
## 55225.33 45631.17 48017.22 52338.11 51529.73 63099.83 62977.44 63267.17
## 666 667 668 669 670 671 672 673
## 51714.31 53074.81 54094.00 49429.42 43404.84 46441.92 44039.01 47528.29
## 674 675 676 677 678 679 680 681
## 45279.12 38111.59 32765.77 32763.27 35514.23 40249.62 42511.66 40514.75
## 682 683 684 685 686 687 688 689
## 40538.36 40987.44 35325.78 41659.70 41156.79 41456.26 43453.17 54168.55
## 690 691 692 693 694 695 696 697
## 62631.88 70686.84 59976.08 55981.01 52861.13 58018.88 48304.83 41999.39
## 698 699 700 701 702 703 704 705
## 35890.76 32607.54 31086.44 36596.99 34859.34 35532.47 41469.62 70149.46
## 706 707 708 709 710 711 712 713
## 76312.41 93871.99 90188.63 92785.74 107821.51 106661.33 84007.20 84354.75
## 714 715 716 717 718 719 720 721
## 75686.99 72787.66 70762.77 53480.31 48875.43 52369.96 49873.01 38980.54
## 722 723 724 725 726 727 728 729
## 44552.41 40821.10 43067.78 40941.49 31642.30 35595.78 36221.46 29852.57
## 730 731 732 733 734 735 736 737
## 47932.80 50181.98 48214.54 53872.33 46273.70 46548.45 54535.34 40977.58
## 738 739 740 741 742 743 744 745
## 37387.13 45894.17 42666.52 44170.14 46940.71 46053.28 42469.95 49463.51
## 746 747 748 749 750 751 752 753
## 44481.23 65457.66 70781.50 66888.21 58818.15 78610.42 71680.05 70598.55
## 754 755 756 757 758 759 760 761
## 55825.90 104578.50 121660.39 126252.41 107766.07 110195.95 109442.90 107470.84
## 762 763 764 765 766 767 768 769
## 60117.32 45158.48 47068.97 45692.61 43667.85 152310.91 156750.70 181971.60
## 770 771 772 773 774 775 776 777
## 185540.44 179474.15 177470.24 184356.90 81507.37 72090.42 38443.16 41878.37
## 778 779 780 781 782 783 784 785
## 42287.62 46688.19 41083.36 41403.56 41246.08 45802.63 40536.48 40233.76
## 786 787 788 789 790 791 792 793
## 45351.05 48377.76 46631.12 43978.69 46719.16 50088.94 50495.06 45035.22
## 794 795 796 797 798 799 800 801
## 41068.62 41653.95 42064.33 36693.40 36775.20 36047.37 35937.97 47547.65
## 802 803 804 805 806 807 808 809
## 50278.64 56895.42 59045.31 53606.07 60772.85 68511.72 57374.98 50444.61
## 810 811 812 813 814 815 816 817
## 44292.90 48105.39 52476.36 50645.92 38647.47 36950.91 44533.68 52889.48
## 818 819 820 821 822 823 824 825
## 44535.18 38915.35 32617.40 43801.85 43980.44 41383.83 35552.07 44723.75
## 826 827 828 829 830
## 52773.59 57239.73 54081.13 53410.50 55974.89
##
## $shapiro.test
## [1] 0
##
## $levenes.test
## [1] 0
##
## $autcorr
## [1] "No autocorrelation evidence"
##
## $post_sums
## [1] "Post-Est Warning"
##
## $adjr_sq
## [1] 0.8104
##
## $fstat.bootstrap
##
## ORDINARY NONPARAMETRIC BOOTSTRAP
##
##
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~
## ., parallel = parr)
##
##
## Bootstrap Statistics :
## original bias std. error
## t1* 5.09459 0.7767181 3.892089
## t2* 2671.56030 167.8014935 896.168024
## WARNING: All values of t3* are NA
##
## $itsa.plot
##
## $booted.ints
## Parameter Lower CI Median F-value Upper CI
## 1 interrupt_var 1.156343 5.052843 13.36502
## 2 lag_depvar 1617.611901 2709.688690 4513.09369Ahora con las tendencias descompuestas
require(zoo)
require(scales)
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>%
dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"Donaciones/regalos",
gasto=="Regalo chocolates"~"Donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
gasto=="Chromecast"~"Electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/mantención casa",
gasto=="Sopapo"~"Electrodomésticos/mantención casa",
gasto=="filtro agua"~"Electrodomésticos/mantención casa",
gasto=="ropa tami"~"Donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"Donaciones/regalos",
gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
gasto=="Reloj"~"Electrodomésticos/mantención casa",
gasto=="Arreglo"~"Electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
#2024
gasto=="cartero"~"Correo",
gasto=="correo"~"Correo",
gasto=="Gaviscón y Paracetamol"~"Farmacia",
gasto=="Regalo Matri Cony"~"Donaciones/regalos",
gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
gasto=="Aporte Basureros"~"Donaciones/regalos",
gasto=="donación"~"Donaciones/regalos",
gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
gasto=="basureros"~"Donaciones/regalos",
gasto=="Microondas regalo"~"Donaciones/regalos",
gasto=="Cruz Verde"~"Farmacia",
gasto=="Remedios Covid"~"Farmacia",
gasto=="nacho"~"Electrodomésticos/mantención casa",
gasto=="Jardinero"~"Electrodomésticos/mantención casa",
gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",
gasto=="Uber cumple papá"~"Transporte",
gasto=="Uber"~"Transporte",
gasto=="Uber Matri Cony"~"Transporte",
gasto=="Bencina + tag"~"Gas/Bencina",
gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
gasto=="Bencina + peajes Maite"~"Gas/Bencina",
gasto=="Crunchyroll"~"Netflix",
gasto=="Crunchyroll"~"Netflix",
gasto=="Incoludido"~"Enceres",
gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
gasto=="Brussels"~"Comida",
gasto=="Tres toques"~"Enceres",
gasto=="Transferencia"~"Otros",
gasto=="prestamo"~"Otros",
gasto=="Préstamo Andrés"~"Otros",
gasto=="mouse"~"Otros",
gasto=="lamina"~"Otros",
T~gasto)) %>%
dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
#dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>%
# dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::summarise(monto=sum(monto)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(size=1) +
facet_grid(gasto~.)+
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
guides(color = F)+
theme_custom() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# Apply MSTL decomposition
mstl_data_autplt <- forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start =
lubridate::decimal_date(as.Date("2019-03-03")))
# Convert the decomposed time series to a data frame
mstl_df <- data.frame(
Date = as.Date(Gastos_casa$fecha, format="%d/%m/%Y"),
Data = as.numeric(mstl_data_autplt[, "Data"]),
Trend = as.numeric(mstl_data_autplt[, "Trend"]),
Remainder = as.numeric(mstl_data_autplt[, "Remainder"])
)
# Reshape the data frame for ggplot2
mstl_long <- mstl_df %>%
pivot_longer(cols = -Date, names_to = "Component", values_to = "Value")
# Plotting with ggplot2
ggplot(mstl_long, aes(x = Date, y = Value)) +
geom_line() +
theme_bw() +
labs(title = "Descomposición MSTL", x = "Fecha", y = "Valor") +
scale_x_date(date_breaks = "3 months", date_labels = "%m-%Y") +
facet_wrap(~ Component, scales = "free_y", ncol = 1) +
theme(strip.text = element_text(size = 12),
axis.text.x = element_text(angle = 90, hjust = 1))
library(bsts)
library(CausalImpact)
ts_week_covid<-
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(fecha_week)%>%
dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
dplyr::ungroup() %>%
dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()
ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]## [1] 98.357 4.780 56.784 50.506 64.483 67.248 49.299 35.786 58.503
## [10] 64.083 20.148 73.476 127.004 81.551 69.599 134.446 58.936 26.145
## [19] 129.927 104.989 130.860 81.893 95.697 64.579 303.471 151.106 49.275
## [28] 76.293 33.940 83.071 119.512 20.942 58.055 71.728 44.090 33.740
## [37] 59.264 77.410 60.831 63.376 48.754 235.284 29.604 115.143 72.419
## [46] 5.980 80.063 149.178 69.918 107.601 72.724 63.203 99.681 130.309
## [55] 195.898 112.066# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na,
state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
niter = 20000,
#burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
seed= 2125)## =-=-=-=-= Iteration 0 Mon Mar 31 00:53:30 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 2000 Mon Mar 31 00:53:39 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 4000 Mon Mar 31 00:53:49 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 6000 Mon Mar 31 00:53:58 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 8000 Mon Mar 31 00:54:07 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 10000 Mon Mar 31 00:54:17 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 12000 Mon Mar 31 00:54:26 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 14000 Mon Mar 31 00:54:35 2025
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## =-=-=-=-= Iteration 16000 Mon Mar 31 00:54:45 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 18000 Mon Mar 31 00:54:54 2025
## =-=-=-=-=#,
# dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")
impact2d1 <- CausalImpact(bsts.model = model1d1,
post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
ylab("Monto Semanal (En miles)")
burn1d1 <- SuggestBurn(0.1, model1d1)corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d <- tm_map(corpus, tolower)
d <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq,
max.words=100, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")
fit_month_gasto <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"Donaciones/regalos",
gasto=="Regalo chocolates"~"Donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
gasto=="Chromecast"~"Electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/mantención casa",
gasto=="Sopapo"~"Electrodomésticos/mantención casa",
gasto=="filtro agua"~"Electrodomésticos/mantención casa",
gasto=="ropa tami"~"Donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"Donaciones/regalos",
gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
gasto=="Reloj"~"Electrodomésticos/mantención casa",
gasto=="Arreglo"~"Electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
#2024
gasto=="cartero"~"Correo",
gasto=="correo"~"Correo",
gasto=="Gaviscón y Paracetamol"~"Farmacia",
gasto=="Regalo Matri Cony"~"Donaciones/regalos",
gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
gasto=="Aporte Basureros"~"Donaciones/regalos",
gasto=="donación"~"Donaciones/regalos",
gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
gasto=="basureros"~"Donaciones/regalos",
gasto=="Microondas regalo"~"Donaciones/regalos",
gasto=="Cruz Verde"~"Farmacia",
gasto=="Remedios Covid"~"Farmacia",
gasto=="nacho"~"Electrodomésticos/mantención casa",
gasto=="Jardinero"~"Electrodomésticos/mantención casa",
gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",
gasto=="Uber cumple papá"~"Transporte",
gasto=="Uber"~"Transporte",
gasto=="Uber Matri Cony"~"Transporte",
gasto=="Bencina + tag"~"Gas/Bencina",
gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
gasto=="Bencina + peajes Maite"~"Gas/Bencina",
gasto=="Crunchyroll"~"Netflix",
gasto=="Crunchyroll"~"Netflix",
gasto=="Incoludido"~"Enceres",
gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
gasto=="Brussels"~"Comida",
gasto=="Tres toques"~"Enceres",
gasto=="Transferencia"~"Otros",
gasto=="prestamo"~"Otros",
gasto=="Préstamo Andrés"~"Otros",
gasto=="mouse"~"Otros",
gasto=="lamina"~"Otros",
T~gasto)) %>%
dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>%
dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>%
dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
data.frame() %>% na.omit()
fit_month_gasto_25<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2025",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_24<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2024",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_23<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2023",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_22<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2022",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_21<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2021",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_20<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2020",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame() %>% ungroup()
fit_month_gasto_25 %>%
dplyr::right_join(fit_month_gasto_24,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_23,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>%
janitor::adorn_totals() %>%
#dplyr::select(-3)%>%
knitr::kable(format = "markdown", size=12, col.names= c("Item","2025","2024","2023","2022","2021","2020"))| Item | 2025 | 2024 | 2023 | 2022 | 2021 | 2020 |
|---|---|---|---|---|---|---|
| Agua | 0.0000 | 6.993667 | 5.195333 | 5.410333 | 5.849167 | 9.93775 |
| Comida | 258.8850 | 326.890000 | 366.009167 | 312.386750 | 317.896583 | 392.93367 |
| Comunicaciones | 0.0000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 |
| Electricidad | 59.9440 | 83.582750 | 38.104750 | 47.072333 | 29.523000 | 20.60458 |
| Enceres | 0.0000 | 23.989000 | 18.259750 | 24.219750 | 14.801167 | 39.01200 |
| Farmacia | 0.0000 | 0.000000 | 10.704083 | 2.835000 | 13.996083 | 14.03675 |
| Gas/Bencina | 0.0000 | 44.292667 | 42.636000 | 45.575000 | 13.583667 | 17.25833 |
| Diosi | 36.9995 | 33.319583 | 55.804250 | 31.180667 | 52.687833 | 37.12133 |
| donaciones/regalos | 0.0000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 |
| Electrodomésticos/ Mantención casa | 0.0000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 |
| VTR | 10.9950 | 18.326667 | 12.829167 | 25.156667 | 19.086917 | 19.11375 |
| Netflix | 0.0000 | 1.391417 | 8.713833 | 7.151583 | 7.028750 | 8.24725 |
| Otros | 0.0000 | 76.164000 | 5.481667 | 5.000000 | 0.000000 | 0.00000 |
| Total | 366.8235 | 614.949750 | 563.738000 | 505.988083 | 474.453167 | 558.26542 |
## Joining with `by = join_by(word)`
2. UF Proyectada
Saqué la UF proyectada
#options(max.print=5000)
uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table")
uf24 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2024.htm")%>% rvest::html_nodes("table")
tryCatch(uf25 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2025.htm")%>% rvest::html_nodes("table"),
error = function(c) {
uf24b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
tryCatch(uf25 <-uf25[[length(uf25)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
error = function(c) {
uf25 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2024, uf23[[length(uf24)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2025, uf25)
)
uf_serie_corrected<-
uf_serie %>%
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>%
dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>%
dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>%
na.omit()#%>% dplyr::filter(is.na(date3))## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
## = T)`.
## Caused by warning:
## ! 54 failed to parse.#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)
warning(paste0("number of observations:",nrow(uf_serie_corrected),", min uf: ",min(uf_serie_corrected$value),", min date: ",min(uf_serie_corrected $date3 )))## Warning: number of observations:2655, min uf: 26799.01, min date: 2018-01-01#
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>%
# dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))
ts_uf_proy<-
ts(data = uf_serie_corrected$value,
start = as.numeric(as.Date("2018-01-01")),
end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats <- forecast::tbats(ts_uf_proy)
fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)# Configurar API Key
nixtlar::nixtla_set_api_key(Sys.getenv("API_NIXTLA"))## API key has been set for the current session.try(nixtlar::nixtla_set_api_key(Sys.getenv("NIXTLA")))## API key has been set for the current session.# Preparar datos en formato requerido por TimeGPT
uf_timegpt <- uf_serie_corrected %>%
dplyr::rename(ds = date3, y = value) %>%
dplyr::mutate(ds = format(ds, "%Y-%m-%d")) %>%
dplyr::mutate(unique_id = "serie_1")%>%
dplyr::select(unique_id, ds, y)
# Realizar pronóstico con TimeGPT
timegpt_fcst <- nixtlar::nixtla_client_forecast(
uf_timegpt,
h = 298, # 298 días a pronosticar
freq = "D", # Frecuencia diaria
add_history = TRUE, # Incluir datos históricos en el output
level = c(80,95),
model= "timegpt-1-long-horizon",
clean_ex_first = TRUE
)## The specified horizon h exceeds the model horizon. This may lead to less accurate forecasts. Please consider using a smaller horizon.# 1. Convertir 'ds' a fecha en ambas tablas
uf_timegpt <- uf_timegpt %>%
mutate(ds = as.Date(ds))
timegpt_fcst <- timegpt_fcst %>%
mutate(ds = as.Date(ds))
# 2. Combinar los datos históricos y el pronóstico
full_data <- bind_rows(
uf_timegpt %>% mutate(type = "Histórico"),
timegpt_fcst %>% mutate(type = "Pronóstico")
)
# Visualizar resultados
ggplot(full_data, aes(x = ds, y = TimeGPT)) +
# Intervalo de confianza del 95%
geom_ribbon(aes(ymin = `TimeGPT-lo-95`, ymax = `TimeGPT-hi-95`),
fill = "#4B9CD3", alpha = 0.2) +
# Intervalo de confianza del 80%
geom_ribbon(aes(ymin = `TimeGPT-lo-80`, ymax = `TimeGPT-hi-80`),
fill = "#4B9CD3", alpha = 0.3) +
# Línea histórica
geom_line(data = filter(full_data, type == "Histórico"),
aes(color = "Histórico"), size = 1) +
# Línea de pronóstico
geom_line(data = filter(full_data, type == "Pronóstico"),
aes(color = "Pronóstico"), size = 1) +
# Línea vertical separadora
geom_vline(xintercept = max(filter(full_data, type == "Histórico")$ds),
linetype = "dashed", color = "red", size = 0.8) +
# Configuración del eje x
scale_x_date(
date_breaks = "3 months", # Reduce la frecuencia de las etiquetas
date_labels = "%b %Y", # Formato de etiquetas (mes y año)
) +
# Configuración del eje y
scale_y_continuous(labels = function(x) format(x, scientific = FALSE)) +
# Configuración de colores
scale_color_manual(
name = "Leyenda",
values = c("Histórico" = "black", "Pronóstico" = "#4B9CD3")
) +
# Títulos y subtítulos
labs(
title = "Pronóstico de Serie Temporal con TimeGPT",
subtitle = "Intervalos de confianza al 80% (más oscuro) y 95% (más claro)",
x = "Fecha",
y = "Valor",
color = "Leyenda"
) +
# Tema y estilos
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1, size = 8),
axis.title.x = element_text(size = 10),
axis.title.y = element_text(size = 10),
legend.position = "bottom",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()
)## Warning: Removed 2655 rows containing missing values or values outside the scale range
## (`geom_line()`).
library(prophet)## Warning: package 'prophet' was built under R version 4.4.3## Loading required package: Rcpp## Warning: package 'Rcpp' was built under R version 4.4.3## Loading required package: rlang## Warning: package 'rlang' was built under R version 4.4.3##
## Attaching package: 'rlang'## The following objects are masked from 'package:purrr':
##
## %@%, flatten, flatten_chr, flatten_dbl, flatten_int, flatten_lgl,
## flatten_raw, invoke, splice## The following object is masked from 'package:sparklyr':
##
## invoke## The following object is masked from 'package:data.table':
##
## := model <- prophet(
cbind.data.frame(ds= as.Date(uf_timegpt$ds), y=uf_timegpt$y),
# Trend flexibility
growth = "linear",
changepoint.prior.scale = 0.05, # Reduced for smoother trend
n.changepoints = 50, # Increased from default 25
# Seasonality
yearly.seasonality = TRUE,
weekly.seasonality = TRUE,
daily.seasonality = FALSE, # Disabled for daily data
seasonality.mode = "additive",
seasonality.prior.scale = 15, # Increased to capture stronger seasonality
# Holidays (if applicable)
# holidays = generated_holidays # Create with add_country_holidays()
# Uncertainty intervals
interval.width = 0.95,
uncertainty.samples = 1000
)
future <- make_future_dataframe(model, periods = 298, include_history = T)
forecast <- predict(model, future)
forecast <- forecast[, c("ds", "yhat", "yhat_lower", "yhat_upper")]
forecast$pred <- ifelse(forecast$ds > max(uf_timegpt$ds), 1,0)## Warning in check_tzones(e1, e2): 'tzone' attributes are inconsistentforecast$ds <- as.Date(forecast$ds)
ggplot(forecast, aes(x = ds, y = yhat)) +
geom_ribbon(aes(ymin = yhat_lower, ymax = yhat_upper),
fill = "#9ecae1", alpha = 0.4) +
geom_line(color = "#08519c", linewidth = 0.8) +
geom_vline(xintercept = max(uf_timegpt$ds), color = "red", linetype = "dashed", linewidth=1) +
scale_x_date(date_breaks = "6 months", date_labels = "%y %b") +
scale_y_continuous(labels = scales::comma) +
labs(title = "Valores predichos (95%IC)",
# subtitle = "March 10, 2025 - May 7, 2025",
x = "Fecha",
y = "Valor",
# caption = "Source: Prophet Forecast Model"
) +
theme_minimal() +
theme(
plot.title = element_text(face = "bold", size = 14),
plot.subtitle = element_text(color = "gray50"),
axis.text.x = element_text(angle = 45, hjust = 1),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
plot.caption = element_text(color = "gray30")
)
La proyección de la UF a 298 días más 2025-04-09 00:04:58 sería de: 26.676 pesos// Percentil 95% más alto proyectado: 35.051,67
Según TimeGPT: La proyección de la UF a 298 días más 2026-02-01 sería de: 40.421,82 pesos// Percentil 80% más alto proyectado: 40.735,27 pesos// Percentil 95% más alto proyectado: 41.080,65
Según prophet: La proyección de la UF a 298 días más 2026-02-01 sería de: 39.289 pesos// Percentil 95% más alto proyectado: 42.197
Ahora con un modelo ARIMA automático
arima_optimal_uf = forecast::auto.arima(ts_uf_proy)
autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq(from = as.Date("2018-01-01"),
to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)),
tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")),
to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
tickmode = "array",
tickangle = 90
))fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | UF Proyectada (TBATS) | UF Proyectada (ARIMA) |
|---|---|---|
| Lo.95 | 26293.96 | 26328.60 |
| Lo.80 | 26425.51 | 26491.43 |
| Point.Forecast | 26675.83 | 26799.01 |
| Hi.80 | 31453.43 | 32108.70 |
| Hi.95 | 34319.77 | 34919.48 |
3. Gastos proyectados
Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.
Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
"link"),skip=1) %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
data.frame()
uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>% dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
data.frame() %>%
dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found
ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)],
start = 1,
end = nrow(uf_serie_corrected_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)],
start = 1,
end = nrow(Gastos_casa_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)
seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")
autplo2t<-
autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2tAhora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.
paste0("Optimo pero sin regresor")## [1] "Optimo pero sin regresor"arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal## Series: ts_gastos_casa_m
## ARIMA(1,1,1)
##
## Coefficients:
## ar1 ma1
## 0.4188 -0.9485
## s.e. 0.1294 0.0592
##
## sigma^2 = 37557: log likelihood = -481.1
## AIC=968.2 AICc=968.55 BIC=975.03paste0("Optimo pero con regresor")## [1] "Optimo pero con regresor"arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2## Series: ts_gastos_casa_m
## Regression with ARIMA(1,0,0) errors
##
## Coefficients:
## ar1 intercept xreg
## 0.4055 562.8354 14.9430
## s.e. 0.1106 328.3026 10.1052
##
## sigma^2 = 35910: log likelihood = -484.98
## AIC=977.96 AICc=978.55 BIC=987.12forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>%
dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>%
dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
data.frame()
autplo2t2<-
autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2t2fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) ## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | Modelo ARIMA con regresor (UF) | Modelo ARIMA sin regresor | Modelo TBATS |
|---|---|---|---|
| Lo.95 | 669.6808 | 643.7799 | 629.0613 |
| Lo.80 | 810.3198 | 795.5792 | 723.3464 |
| Point.Forecast | 1075.9932 | 1083.7927 | 948.1711 |
| Hi.80 | 1341.6666 | 1375.4911 | 1241.8993 |
| Hi.95 | 1482.3056 | 1529.9070 | 1432.1579 |
4. Gastos mensuales (resumen manual)
path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")
Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
#col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
skip=0)## Rows: 80 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Tami, Andrés
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.head(Gastos_casa_mensual_2022,5) %>%
knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")| n | mes_ano | Tami | Andrés |
|---|---|---|---|
| 1 | marzo_2019 | 175533 | 68268 |
| 2 | abril_2019 | 152640 | 55031 |
| 3 | mayo_2019 | 152985 | 192219 |
| 4 | junio_2019 | 291067 | 84961 |
| 5 | julio_2019 | 241389 | 205893 |
(
Gastos_casa_mensual_2022 %>%
reshape2::melt(id.var=c("n","mes_ano")) %>%
dplyr::mutate(gastador=as.factor(variable)) %>%
dplyr::select(-variable) %>%
ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
scale_color_manual(name="Gastador", values=c("red", "blue"))+
geom_line(size=1) +
#geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
ggtitle( "Gastos Mensuales (total manual)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
# scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
# scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
# guides(color = F)+
theme_custom() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
) %>% ggplotly()Gastos_casa_mensual_2022$mes_ano <- gsub("marzo", "Mar", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("abril", "Apr", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("mayo", "May", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("junio", "Jun", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("julio", "Jul", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("agosto", "Aug", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("septiembre", "Sep", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("octubre", "Oct", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("noviembre", "Nov", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("diciembre", "Dec", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("enero", "Jan", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("febrero", "Feb", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022<- dplyr::filter(Gastos_casa_mensual_2022, !is.na(Tami))
Gastos_casa_mensual_2022$mes_ano <- parse_date_time(Gastos_casa_mensual_2022$mes_ano, "%b_%Y")
Gastos_casa_mensual_2022$mes_ano <- as.Date(as.character(Gastos_casa_mensual_2022$mes_ano))
Gastos_casa_mensual_2022_timegpt <- Gastos_casa_mensual_2022 %>%
mutate(value = Tami + Andrés) %>%
rename(ds = mes_ano, y = value) %>%
mutate(#ds= format(ds, "%Y-%m"),
unique_id = "1") %>% #it is only one series
select(unique_id, ds, y)
#Convertir la base de UF a mensual
uf_timegpt_my <- uf_serie_corrected %>%
dplyr::rename(ds = date3, y = value) %>%
dplyr::mutate(ds = format(ds, "%Y-%m-%d")) %>%
dplyr::mutate(unique_id = "serie_1")%>%
dplyr::select(unique_id, ds, y) %>%
mutate(ds = ymd(ds)) %>% # Convert 'ds' to Date
mutate(month = month(ds), year = year(ds)) %>% # Extract month and year
group_by(month, year) %>% # Group by month and year
summarise(average_y = mean(y))%>% # Calculate average y
mutate(ds = as.Date(paste0(year,"-",month, "-01")))%>%
ungroup()%>%
select(ds, uf=average_y)
Gastos_casa_mensual_2022_timegpt_ex<-
Gastos_casa_mensual_2022_timegpt |>
dplyr::left_join(uf_timegpt_my, by=c("ds"="ds"))
#Historical Exogenous Variables: These should be included in the input data immediately following the id_col, ds, and y columns
gastos_timegpt_fcst <- nixtlar::nixtla_client_forecast(
Gastos_casa_mensual_2022_timegpt_ex,
h = 12,
freq = "M", # Monthly frequency
add_history = TRUE,
level = c(80, 95),
model = "timegpt-1",#"timegpt-1-long-horizon",
clean_ex_first = TRUE
)
# Convert 'ds' to Date format in both tables
Gastos_casa_mensual_2022_timegpt_corr <- Gastos_casa_mensual_2022_timegpt %>%
mutate(ds = as.Date(paste0(ds, "-01"))) # Add day to make it a complete date
gastos_timegpt_fcst <- gastos_timegpt_fcst %>%
mutate(ds = as.Date(paste0(ds, "-01"))) # Add day to make it a complete date
# Combine historical and forecast data
full_data_gastos <- bind_rows(
Gastos_casa_mensual_2022_timegpt_corr %>% mutate(type = "Histórico"),
gastos_timegpt_fcst %>% mutate(type = "Pronóstico")
)
full_data_gastos |>
dplyr::mutate(y= ifelse(is.na(y),TimeGPT, y)) |>
# Visualize results
ggplot(aes(x = ds, y = y)) +
geom_ribbon(aes(ymin = `TimeGPT-lo-95`, ymax = `TimeGPT-hi-95`),
fill = "#4B9CD3", alpha = 0.2) +
geom_ribbon(aes(ymin = `TimeGPT-lo-80`, ymax = `TimeGPT-hi-80`),
fill = "#4B9CD3", alpha = 0.3) +
geom_line(aes(color = type), linewidth = 1.5) +
geom_vline(xintercept = max(filter(full_data_gastos, type == "Histórico")$ds),
linetype = "dashed", color = "red", linewidth = 0.8) +
scale_x_date(
date_breaks = "3 months",
date_labels = "%b %Y"
) +
scale_y_continuous(
name = "Gastos Totales",
labels = scales::comma,
breaks = pretty(full_data_gastos$y, n = 10),
expand = expansion(mult = c(0.05, 0.05))
) +
scale_color_manual(
name = "Leyenda",
values = c("Histórico" = "black", "Pronóstico" = "#4B9CD3")
) +
labs(
title = "Pronóstico de Gastos Mensuales (TimeGPT, ajustando por UF promedio mensual)",
subtitle = "Intervalos de confianza al 80% (más oscuro) y 95% (más claro)",
x = "Fecha",
y = "Gastos Totales",
color = "Leyenda"
) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
axis.title.x = element_text(size = 10),
axis.title.y = element_text(size = 10),
legend.position = "bottom",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()
)
Session Info
Sys.getenv("R_LIBS_USER")## [1] "D:\\a\\_temp\\Library"sessionInfo()## R version 4.4.0 (2024-04-24 ucrt)
## Platform: x86_64-w64-mingw32/x64
## Running under: Windows Server 2022 x64 (build 20348)
##
## Matrix products: default
##
##
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252 LC_CTYPE=Spanish_Chile.1252
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C
## [5] LC_TIME=Spanish_Chile.1252
## system code page: 65001
##
## time zone: UTC
## tzcode source: internal
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] prophet_1.0 rlang_1.1.5 Rcpp_1.0.14
## [4] CausalImpact_1.3.0 bsts_0.9.10 BoomSpikeSlab_1.2.6
## [7] Boom_0.9.15 scales_1.3.0 ggiraph_0.8.13
## [10] tidytext_0.4.2 DT_0.33 autoplotly_0.1.4
## [13] rvest_1.0.4 plotly_4.10.4 xts_0.14.1
## [16] forecast_8.23.0 wordcloud_2.6 RColorBrewer_1.1-3
## [19] SnowballC_0.7.1 tm_0.7-16 NLP_0.3-2
## [22] tsibble_1.1.6 lubridate_1.9.4 forcats_1.0.0
## [25] dplyr_1.1.4 purrr_1.0.4 tidyr_1.3.1
## [28] tibble_3.2.1 ggplot2_3.5.1 tidyverse_2.0.0
## [31] sjPlot_2.8.17 lattice_0.22-6 gridExtra_2.3
## [34] plotrix_3.8-4 sparklyr_1.9.0 httr_1.4.7
## [37] readxl_1.4.5 zoo_1.8-13 stringr_1.5.1
## [40] stringi_1.8.7 DataExplorer_0.8.3 data.table_1.17.0
## [43] reshape2_1.4.4 fUnitRoots_4040.81 plyr_1.8.9
## [46] readr_2.1.5
##
## loaded via a namespace (and not attached):
## [1] bitops_1.0-9 cellranger_1.1.0 datawizard_1.0.2
## [4] janitor_2.2.1 lifecycle_1.0.4 httr2_1.1.2
## [7] StanHeaders_2.32.10 globals_0.16.3 vroom_1.6.5
## [10] MASS_7.3-60.2 insight_1.1.0 crosstalk_1.2.1
## [13] magrittr_2.0.3 sass_0.4.9 rmarkdown_2.29
## [16] jquerylib_0.1.4 yaml_2.3.10 fracdiff_1.5-3
## [19] askpass_1.2.1 pkgbuild_1.4.7 DBI_1.2.3
## [22] abind_1.4-8 quadprog_1.5-8 nnet_7.3-19
## [25] rappdirs_0.3.3 inline_0.3.21 tokenizers_0.3.0
## [28] listenv_0.9.1 anytime_0.3.11 performance_0.13.0
## [31] spatial_7.3-17 parallelly_1.43.0 codetools_0.2-20
## [34] xml2_1.3.8 tidyselect_1.2.1 ggeffects_2.2.1
## [37] farver_2.1.2 urca_1.3-4 its.analysis_1.6.0
## [40] matrixStats_1.5.0 stats4_4.4.0 jsonlite_2.0.0
## [43] ellipsis_0.3.2 Formula_1.2-5 systemfonts_1.2.1
## [46] tools_4.4.0 glue_1.8.0 xfun_0.51
## [49] TTR_0.24.4 ggfortify_0.4.17 loo_2.8.0
## [52] withr_3.0.2 timeSeries_4041.111 fastmap_1.2.0
## [55] boot_1.3-30 openssl_2.3.2 caTools_1.18.3
## [58] digest_0.6.37 timechange_0.3.0 R6_2.6.1
## [61] colorspace_2.1-1 networkD3_0.4 gtools_3.9.5
## [64] generics_0.1.3 htmlwidgets_1.6.4 pkgconfig_2.0.3
## [67] gtable_0.3.6 timeDate_4041.110 lmtest_0.9-40
## [70] selectr_0.4-2 janeaustenr_1.0.0 htmltools_0.5.8.1
## [73] carData_3.0-5 tseries_0.10-58 snakecase_0.11.1
## [76] knitr_1.50 rstudioapi_0.17.1 tzdb_0.5.0
## [79] uuid_1.2-1 nlme_3.1-164 curl_6.2.2
## [82] cachem_1.1.0 sjlabelled_1.2.0 KernSmooth_2.23-22
## [85] parallel_4.4.0 fBasics_4041.97 pillar_1.10.1
## [88] vctrs_0.6.5 gplots_3.2.0 slam_0.1-55
## [91] car_3.1-3 dbplyr_2.5.0 evaluate_1.0.3
## [94] cli_3.6.4 compiler_4.4.0 crayon_1.5.3
## [97] future.apply_1.11.3 labeling_0.4.3 sjmisc_2.8.10
## [100] rstan_2.32.7 QuickJSR_1.6.0 viridisLite_0.4.2
## [103] assertthat_0.2.1 munsell_0.5.1 lazyeval_0.2.2
## [106] Matrix_1.7-0 sjstats_0.19.0 hms_1.1.3
## [109] bit64_4.6.0-1 future_1.34.0 nixtlar_0.6.2
## [112] extraDistr_1.10.0 igraph_2.1.4 RcppParallel_5.1.10
## [115] bslib_0.9.0 quantmod_0.4.26 bit_4.6.0#save.image("__analisis.RData")
sesion_info <- devtools::session_info()
dplyr::select(
tibble::as_tibble(sesion_info$packages),
c(package, loadedversion, source)
) %>%
DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: left;',
'', htmltools::em('Packages')),
options=list(
initComplete = htmlwidgets::JS(
"function(settings, json) {",
"$(this.api().tables().body()).css({
'font-family': 'Helvetica Neue',
'font-size': '50%',
'code-inline-font-size': '15%',
'white-space': 'nowrap',
'line-height': '0.75em',
'min-height': '0.5em'
});",#;
"}")))